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models, which are essential for understanding climate change impacts. The work involves reviewing existing modeling and model–data fusion techniques, and developing faster, machine-learning–based tools
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pangenomics), quantum-based simulation methods for drug design, or quantum machine learning for large omics datasets. The candidate is expected to have acquired first teaching experience, and first experience
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that after obtaining a PhD, eligible candidates for research associate appointments may not exceed a combined total of 5 years of relevant work experience as a post-doc and/or in an R&D position, excluding
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physical models. As the PhD researcher on this project, you will work at the intersection of machine learning, geometry processing and industrial simulation. You will have the opportunity to explore
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physiological signals, with a focus on ECG, and to develop machine learning and deep learning methods for classifying clinical, health, and wellness findings. Supporting project management and research group
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Kepler University Linz (JKU). Check out the detailed announcement on our homepage: https://www.pro2future.at/phd-candidate-within-the-topic-ai-driven-software-instrumentation/ Where to apply E-mail jobs
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science methodologies (e.g., machine learning). Experience working with large-scale environmental and remotely-sensed datasets, strong proficiency in R and version control tools (such as GitHub), and
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the PhD program (https://www.utrgv.edu/cla/academic-programs/clinical-pyschology-phd-program/index.htm) mentor graduate students in the PhD program in clinical psychology, and serve as a clinical supervisor
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imaging and machine learning. The main task of the successful candidate will be to help redefine certain traditional criteria of comparative anatomy used in archaeozoology and to establish new criteria
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emerging spectrum (FR3, MMW, sub-THz/THz) Extreme massive MIMO communications Analog, digital and hybrid beamforming architectures Reconfigurable intelligent surfaces Machine learning for wireless